Abstract
Methods for characterizing video segments and allowing fast search in large archives are becoming essential in the video information flood. In this paper, we present a method for characterizing and clustering video segments using cumulative color histogram. The underlying assumption is that a video segment has a consistent color palette, which can be derived as a family of merged individual shot histograms. These merged histograms (SuperHistograms) are clustered using a Nearest Neighbor-clustering algorithm. Given a query video, in order to find similar videos, the SuperHistogram of the video will be generated and compared to the centers of the Nearest Neighbor clusters. The video clips in the cluster with center nearest to the query, can be searched to find video clips most similar to the query video. This method can be used in a variety of applications that need video classification and retrieval methods such as video editing, video archival, digital libraries, consumer products, and web crawling.
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References
M. Abdel-Mottaleb, N. Dimitrova, R. Desai and J. Martino, “CONIVAS: Cotent-based Image and Video Access System,” Proc. of ACM Multimedia, 1996, pp 427–428
M. Abdel-Mottaleb and R. Desai, “Image Retrieval by Local Color Features,” The Fourth IEEE Symposium on Computers and Communications, Egypt, July 1999.
S-F. Chang, W. Chen, H. E. Meng, H. Sundaram and D. Zong, “VideoQ: An Automated Content Based Video Search System Using Visual Cues,” Proc. ACM Multimedia, pp.313–324, Seattle, 1997.
M. Christel, T. Kanade, M. Mauldin, R. Reddy, M. Sirbu, S. Stevens and H. Wactlar, “Informedia Digital Video Library,” Comm. of the ACM, Vol. 38, No. 4, 1995, p57–58.
Belur V. Dasarathy, “Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques,” IEEE Computer Society Press, 1991.
N. Dimitrova, J. Martino, L. Agnihotri, and H. Elenbaas, “Color SuperHistograms for Video Representation,” Int. Conf. on Image Processing, Japan, 1999.
R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley Publishing Co., Inc., 1992.
Santhana Krishnamachari, and Mohamed Abdel-Mottaleb, “Heirarchical Clustering Algorithm for Fast Image Retrieval,” Proc. IS&T SPIE, Storage and Retrieval for Image and Video Databases VII, Volume 3312, pp. 427–435, San Jose, 1999.
M.K. Mandal and T. Aboulnasr and S. Panchanatan, “Image indexing using moments and wavelets,” IEEE Transactions on Consumer Electronics, Vol. 42, No. 3, August 1996.
A. Nagasaka and Y. Tanaka, “Automatic Video Indexing and Full Video Search for Object Appearances,” Visual Database Systems II, Elsevier Sci. Pub., 1991, pp. 113 127.
W. Niblack, X. Zhu, J.L. Hafner, T. Bruel, D. B. Ponceleon, D. Petkovic, M. Flickner, E. Upfal, S.I. Nin, . Sull, B.E. Dom, “Updates to the QBIC System,” Proc. IS&T SPIE, Storage and Retrieval for Image and Video Databases VI, Volume 3312, pp. 150–161, San Jose, 1998.
M. T. Orchard and C. A. Bouman, “Color Quantization of Images,” IEEE Trans. Signal Proc., Vol 39, No. 12, 1991.
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© 2000 Springer-Verlag Berlin Heidelberg
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Agnihotri, L., Dimitr, N. (2000). Video Clustering Using SuperHistograms in Large Archives. In: Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2000. Lecture Notes in Computer Science, vol 1929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40053-2_6
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DOI: https://doi.org/10.1007/3-540-40053-2_6
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